1. Field of the Invention
The present invention relates to an apparatus and method for estimating the pose of a mobile robot, and, more particularly, to an apparatus and method for estimating the pose of a mobile robot using a particle filter.
2. Description of the Related Art
Recently, various types of robots for use in industry, homes and businesses, such as cleaning robots, guidance robots and security robots, have been commercialized.
Robots can perform functions while autonomously traveling within a given space. In order for a robot to perform functions while traveling in a given environment, a map of the robot's surrounding needs to be provided, and information regarding the position and the heading angle of the robot is required. Simultaneous Localization and Mapping (SLAM) algorithms may be used to construct a map and to locate the robot.
SLAM algorithms are characterized by repeatedly updating the map of a robot's surroundings, and determining the position of the robot with reference to the map.
SLAM algorithms may use a particle filter technique for locating a robot. The particle filter technique involves configuring a plurality of samples regarding the position and the heading angle of a robot, calculating the probabilities of the samples and estimating the optimum pose of the robot based on the results of the calculation. The term “pose” as used herein denotes the position and the heading angle of an object in a 2-dimensional (2D) coordinate system.
SLAM algorithms may be classified into SLAM algorithms using feature points and SLAM algorithms using raw data such as range data.
SLAM algorithms using feature points may be inefficient because it is generally difficult to extract corner points. Thus, SLAM algorithms using feature points may result in errors, especially when used in an environment lacking prominent feature points. For example, it is difficult to extract corner points from a room with a white ceiling and white ceiling lights since it is difficult to distinguish the white ceiling from the white ceiling lights.
SLAM techniques using feature points may involve determining whether current feature points are identical to previous feature points. However, it is difficult to determine whether current feature points are identical to previous feature points, especially in the presence of a data-association-error accumulation.
When a robot continuously moves and thus the pose of the mobile robot changes, an error between the pose of the mobile robot estimated by an odometer and the actual pose of the mobile robot may become greater than a predefined threshold. For example, when a mobile robot travels on a carpeted floor or on a slippery floor, the estimated pose may deviate considerably from the actual pose of the mobile robot.
Therefore, what is needed is a pose-estimation device which can be embedded in a mobile robot, and can effectively estimate, using a small amount of memory, the pose of the mobile robot regardless of an odometer error and the difficulty of extracting feature points.